Hi Tim
I don't think there's a general relationship as it will depend on the
geometry of the lesions and of the cortical surface. I guess what I would
do is threshold the lesions in the volume, map them to the surface and see
what the statistics of their areas are, then use that for thresholding. Or
just try some different thresholds and see what seems reasonable to you in
terms of false positives/false negatives
cheers
Bruce
On Sun, 23 Nov 2014,
Timothy Meier wrote:
Hi Bruce,
Sorry, I did a poor job of describing the problem.
We are trying to do an analysis of subject specific abnormalities on the
surface. Instead of a traditional analysis assuming that patients have
homogenous abnormalities (for example cortical thinning in anterior
cingulate) relative to a control group, we want to count the number of
abnormalities over the entire surface in each subject. On the surface,
abnormalities can be defined as average thickness in a small region that is
>2 SD away from mean thickness in a control group in that same region.
Following that, we can compare the average number of subject specific
abnormalities between groups.
This method has been used to identify white matter abnormalities in patient
populations in which heterogenous abnormalities are expected, such as TBI
patients. However, it requires an a priori decision about what constitutes
an abnormality. In this case, we need to specify it in number of vertices.
We have previously used 128 ul as our defined abnormality size in the
volume. Our first thought was to use that as a starting point and translate
the volume to surface area or number of vertices.
Is there a reasonable way to approximate how many voxels get translated into
surface space? And is there a reasonable way to approximate how much area
each data point would have on the surface?
One approach we are considering is to calculate the average gray matter
volume, surface area, and number of vertices from the aparc.stats file. With
this information we'd have an approximate relationship between volume,
surface area, and # vertices. We are curious if this seems like a reasonable
choice, or if you have any thoughts on how to go about this.
Thanks a lot,
Tim
On Fri, Nov 21, 2014 at 11:00 AM, <freesurfer-requ...@nmr.mgh.harvard.edu>
wrote:
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Today's Topics:
1. Re: Repost: estimating lesion size on surface (Bruce
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4. Re: Odd thing about mris_convert? Position changed from
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----------------------------------------------------------------------
Message: 1
Date: Thu, 20 Nov 2014 16:45:17 -0500 (EST)
From: Bruce Fischl <fis...@nmr.mgh.harvard.edu>
Subject: Re: [Freesurfer] Repost: estimating lesion size on
surface
To: Freesurfer support list <freesurfer@nmr.mgh.harvard.edu>
Message-ID:
<alpine.lrh.2.11.1411201644480.6...@gate.nmr.mgh.harvard.edu>
Content-Type: text/plain; charset="utf-8"
Hi Tim
can you elaborate what the issue is? If you have binary lesion
maps inthe
volume, can't you just sample those onto the surface and measure
surface
area directly? I think I am missing something
Bruce
On Wed, 19 Nov 2014, Timothy
Meier wrote:
> Hello,
> I just wanted to repost this to see if anyone had any thoughts
on an
> appropriate 'lesion' size for a pothole analysis performed on
the surface.
> We want to compare the average number of small surface lesions
(estimated
> from cortical thickness) across single subjects for a control
group relative
> to our patient group.
>
> We have done similar analyses in the volume for white matter,
so one thought
> was to use that same lesion size. However, that requires
estimating a
> surface area from our previously used volume.
>
> Any thoughts on how to approach this, or thoughts on an
appropriate lesion
> size to use would be appreciated.
>
> Thanks,
>
> -Tim
>
>
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